Dynamic textures
Dynamic textures
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DOI：
10.1023/a:1021669406132
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发表时间：
2003-02-01
影响因子：
19.5
通讯作者：
Soatto, S
中科院分区：
文献类型：
--
作者：
Doretto, G;Chiuso, A;Soatto, S
动态纹理是序列的图像的移动场景，表现出一定的平稳性属性的时间，这些包括海浪，烟雾，树叶，旋风等，我们提出了一个动态纹理的表征，提出了建模，学习，识别和合成动态纹理的问题在一个坚实的分析基础。我们借用系统识别的工具来捕捉动态纹理的“本质”;我们通过学习（即识别）在最大似然或最小预测误差方差意义上最优的模型来做到这一点。对于二阶平稳过程的特殊情况下，我们确定的封闭形式的次优模型。一旦学习，模型就具有预测能力，并且可以用于将合成序列外推到无限长，而计算成本可以忽略不计。我们目前的实验证据表明，在我们的框架内，即使是低维模型可以捕捉非常复杂的视觉现象。
Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time; these include sea-waves, smoke, foliage, whirlwind etc. We present a characterization of dynamic textures that poses the problems of modeling, learning, recognizing and synthesizing dynamic textures on a firm analytical footing. We borrow tools from system identification to capture the "essence" of dynamic textures; we do so by learning (i.e. identifying) models that are optimal in the sense of maximum likelihood or minimum prediction error variance. For the special case of second-order stationary processes, we identify the model sub-optimally in closed-form. Once learned, a model has predictive power and can be used for extrapolating synthetic sequences to infinite length with negligible computational cost. We present experimental evidence that, within our framework, even low-dimensional models can capture very complex visual phenomena.